





"""simple docstring"""





import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
       from ...tokenization_utils_base import TextInput
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE__   =  {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
SCREAMING_SNAKE_CASE__   =  {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}

SCREAMING_SNAKE_CASE__   =  "▁"

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE							      = ['input_ids', 'attention_mask']





      def __init__(			self ,		lowercase ,		lowercase="</s>" ,		lowercase="<unk>" ,		lowercase="<pad>" ,		lowercase=100 ,		lowercase=None ,		lowercase = None ,		lowercase=True ,		**lowercase ,		)						->	None:
            # Add extra_ids to the special token list
            if extra_ids > 0 and additional_special_tokens is None:
                  lowerCAmelCase    		=			[f'<extra_id_{i}>' for i in range(lowercase					)]
            elif extra_ids > 0 and additional_special_tokens is not None:
                  # Check that we have the right number of extra_id special tokens
                  lowerCAmelCase    		=			len(set(filter(lambda lowercase					: bool("""extra_id""" in str(lowercase					)					) ,		lowercase					)					)					)
                  if extra_tokens != extra_ids:
                        raise ValueError(
                            f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                            """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
                            """ tokens"""					)
            if legacy:
                  logger.warning_once(
                      f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
                      """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""					)

            lowerCAmelCase    		=			legacy
            lowerCAmelCase    		=			{} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                eos_token=lowercase ,		unk_token=lowercase ,		pad_token=lowercase ,		extra_ids=lowercase ,		additional_special_tokens=lowercase ,		sp_model_kwargs=self.sp_model_kwargs ,		legacy=lowercase ,		**lowercase ,		)

            lowerCAmelCase    		=			vocab_file
            lowerCAmelCase    		=			extra_ids

            lowerCAmelCase    		=			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(lowercase					)





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase ,		lowercase					)						->	Optional[int]:
            if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
                  lowerCAmelCase    		=			TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
                  if init_max_model_length is not None and init_max_model_length != max_model_length:
                        return init_max_model_length
                  elif init_max_model_length is None:
                        warnings.warn(
                            """This tokenizer was incorrectly instantiated with a model max length of"""
                            f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                            """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
                            """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
                            f' {pretrained_model_name_or_path} automatically truncating your input to'
                            f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                            f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                            """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
                            """ instantiate this tokenizer with `model_max_length` set to your preferred value.""" ,		lowercase ,		)

            return max_model_length





      @property
      def        _snake_case     (			self					)						->	List[str]:
            return self.sp_model.get_piece_size() + self._extra_ids





      def        _snake_case     (			self					)						->	Optional[Any]:
            lowerCAmelCase    		=			{self.convert_ids_to_tokens(lowercase					): i for i in range(self.vocab_size					)}
            vocab.update(self.added_tokens_encoder					)
            return vocab





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		lowercase = False					)						->	List[int]:
            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=lowercase ,		token_ids_a=lowercase ,		already_has_special_tokens=lowercase					)

            # normal case: some special tokens
            if token_ids_a is None:
                  return ([0] * len(lowercase					)) + [1]
            return ([0] * len(lowercase					)) + [1] + ([0] * len(lowercase					)) + [1]





      def        _snake_case     (			self					)						->	Optional[int]:
            return list(
                set(filter(lambda lowercase					: bool(re.search(r"""<extra_id_\d+>""" ,		lowercase					)					) is not None ,		self.additional_special_tokens					)					)					)





      def        _snake_case     (			self					)						->	int:
            return [self._convert_token_to_id(lowercase					) for token in self.get_sentinel_tokens()]





      def        _snake_case     (			self ,		lowercase					)						->	List[int]:
            if len(lowercase					) > 0 and token_ids[-1] == self.eos_token_id:
                  warnings.warn(
                      f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
                      """ eos tokens being added."""					)
                  return token_ids
            else:
                  return token_ids + [self.eos_token_id]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	List[int]:
            lowerCAmelCase    		=			[self.eos_token_id]

            if token_ids_a is None:
                  return len(token_ids_a + eos					) * [0]
            return len(token_ids_a + eos + token_ids_a + eos					) * [0]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	List[int]:
            lowerCAmelCase    		=			self._add_eos_if_not_present(lowercase					)
            if token_ids_a is None:
                  return token_ids_a
            else:
                  lowerCAmelCase    		=			self._add_eos_if_not_present(lowercase					)
                  return token_ids_a + token_ids_a





      def __getstate__(			self					)						->	int:
            lowerCAmelCase    		=			self.__dict__.copy()
            lowerCAmelCase    		=			None
            return state





      def __setstate__(			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			d

            # for backward compatibility
            if not hasattr(self ,		"""sp_model_kwargs"""					):
                  lowerCAmelCase    		=			{}

            lowerCAmelCase    		=			spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(self.vocab_file					)





      def        _snake_case     (			self ,		lowercase ,		**lowercase					)						->	List[str]:
            # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
            # the beginning of the text
            if not self.legacy:
                  lowerCAmelCase    		=			SPIECE_UNDERLINE + text.replace(lowercase ,		""" """					)
            return super().tokenize(lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		**lowercase					)						->	List[str]:
            if not self.legacy:
                  lowerCAmelCase    		=			text.startswith(lowercase					)
                  if is_first:
                        lowerCAmelCase    		=			text[1:]

            lowerCAmelCase    		=			self.sp_model.encode(lowercase ,		out_type=lowercase					)

            if not self.legacy and not is_first and not text.startswith(""" """					) and tokens[0].startswith(lowercase					):
                  lowerCAmelCase    		=			([tokens[0][1:]] if len(tokens[0]					) > 1 else []) + tokens[1:]
            return tokens





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            if token.startswith("""<extra_id_"""					):
                  lowerCAmelCase    		=			re.match(r"""<extra_id_(\d+)>""" ,		lowercase					)
                  lowerCAmelCase    		=			int(match.group(1					)					)
                  return self.vocab_size - num - 1
            return self.sp_model.piece_to_id(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	str:
            if index < self.sp_model.get_piece_size():
                  lowerCAmelCase    		=			self.sp_model.IdToPiece(lowercase					)
            else:
                  lowerCAmelCase    		=			f'<extra_id_{self.vocab_size - 1 - index}>'
            return token





      def        _snake_case     (			self ,		lowercase					)						->	str:
            lowerCAmelCase    		=			[]
            lowerCAmelCase    		=			""""""
            lowerCAmelCase    		=			False
            for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                        if not prev_is_special:
                              out_string += " "
                        out_string += self.sp_model.decode(lowercase					) + token
                        lowerCAmelCase    		=			True
                        lowerCAmelCase    		=			[]
                  else:
                        current_sub_tokens.append(lowercase					)
                        lowerCAmelCase    		=			False
            out_string += self.sp_model.decode(lowercase					)
            return out_string.strip()





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	Tuple[str]:
            if not os.path.isdir(lowercase					):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
                  return
            lowerCAmelCase    		=			os.path.join(
                lowercase ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

            if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase					) and os.path.isfile(self.vocab_file					):
                  copyfile(self.vocab_file ,		lowercase					)
            elif not os.path.isfile(self.vocab_file					):
                  with open(lowercase ,		"""wb"""					) as fi:
                        lowerCAmelCase    		=			self.sp_model.serialized_model_proto()
                        fi.write(lowercase					)

            return (out_vocab_file,)













"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			list(s_dict.keys()     )
      for key in keys:
            if "transformer_layers" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )
            elif "subsample" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			emb.weight.shape
      lowerCAmelCase    		=			nn.Linear(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		bias=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			emb.weight.data
      return lin_layer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )
      lowerCAmelCase    		=			mam_aaa["""args"""]
      lowerCAmelCase    		=			mam_aaa["""model"""]
      lowerCAmelCase    		=			state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
      rename_keys(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			state_dict["""decoder.embed_tokens.weight"""].shape[0]

      lowerCAmelCase    		=			args.share_decoder_input_output_embed

      lowerCAmelCase    		=			[int(SCREAMING_SNAKE_CASE     ) for i in args.conv_kernel_sizes.split(""","""     )]
      lowerCAmelCase    		=			SpeechaTextConfig(
          vocab_size=SCREAMING_SNAKE_CASE				,		max_source_positions=args.max_source_positions				,		max_target_positions=args.max_target_positions				,		encoder_layers=args.encoder_layers				,		decoder_layers=args.decoder_layers				,		encoder_attention_heads=args.encoder_attention_heads				,		decoder_attention_heads=args.decoder_attention_heads				,		encoder_ffn_dim=args.encoder_ffn_embed_dim				,		decoder_ffn_dim=args.decoder_ffn_embed_dim				,		d_model=args.encoder_embed_dim				,		dropout=args.dropout				,		attention_dropout=args.attention_dropout				,		activation_dropout=args.activation_dropout				,		activation_function="""relu"""				,		num_conv_layers=len(SCREAMING_SNAKE_CASE     )				,		conv_channels=args.conv_channels				,		conv_kernel_sizes=SCREAMING_SNAKE_CASE				,		input_feat_per_channel=args.input_feat_per_channel				,		input_channels=args.input_channels				,		tie_word_embeddings=SCREAMING_SNAKE_CASE				,		num_beams=5				,		max_length=2_00				,		use_cache=SCREAMING_SNAKE_CASE				,		decoder_start_token_id=2				,		early_stopping=SCREAMING_SNAKE_CASE				,		)

      lowerCAmelCase    		=			SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			model.model.load_state_dict(SCREAMING_SNAKE_CASE				,		strict=SCREAMING_SNAKE_CASE     )
      if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                F' but all the following weights are missing {missing}'     )

      if tie_embeds:
            lowerCAmelCase    		=			make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            lowerCAmelCase    		=			lm_head_weights

      model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






